Incidence of children with autism spectrum disorder (ASD) has increased with an average rate of 1% worldwide. Clinical ASD screening, especially for children screening is a laborious and skilled task, there is however no objective and effective method automating ASD children screening. Analysing children ASD characteristics in predefined motion behaviour protocols is attempted to provide automatic solutions to children ASD screening. A novel protocol, Response to Name (RTN), is proposed in this paper for ASD clinical validation and diagnosis. The RTN method is jointly designed with clinical partners, novel gaze estimation is developed for validating ASD characteristic behaviour. Seventeen subjects including 10 adults and 7 children (5 ASD subjects and 2 healthy subjects) participated the experiment. The experiment results showed that the proposed RTN system achieves an average classification score of 92.7% fully demonstrating that the principle of motion protocol based ASD screening has the potential to have early ASD screening automated.

Objective: Autism Spectrum Disorders (ASD) are characterized by impairments in joint attention (JA) comprising two components: responding to JA (RJA) and initiating JA (IJA). RJA and IJA are considered two interrelated aspects of JA, related to different stages of infant development. While recent technologies have been used to characterize RJA emerging in earlier childhood, only a limited number of studies have attempted to explore IJA, which progressively becomes evident as a hallmark of ASD. This study aims to achieve the social recognition of both RJA and IJA by vision-based human behavior perception through a multi-modal framework automatically and comprehensively. Methods: The first three layers of this framework leverage localization, feature extraction, and activity recognition. On this basis, three critical activities in JA are recognized: attention estimation, spontaneous pointing, and showing actions. Then different behaviors are linked through the fourth layer, semantic interpretation, to model the JA event. The proposed framework is evaluated on experiments of four groups: 7 children with ASD, 5 children with mental retardation (MR), 5 children with developmental language disorder (DLD), and 3 typically developed children (TD). Results: Experimental results compared with human codings demonstrate recognition reliability with an intra-class coefficient of 0.959. In addition, statistical analysis suggests significant group difference and correlations. Conclusions: The multi-modal human behavior perception-based framework is a feasible solution for the recognition of joint attention in unconstrained environments. Significance: Thus the proposed approach has the potential to improve the clinical diagnosis of autism by offering quantitative monitoring and statistical analysis.

Interpersonal communication facilitates symptom measures of autistic sociability to enhance clinical decision-making in identifying children with autism spectrum disorder (ASD). Traditional methods are carried out by clinical practitioners with assessment scales, which are subjective to quantify. Recent studies employ engineering technologies to analyze children's behaviors with quantitative indicators, but these methods only generate specific rule-driven indicators that are not adaptable to diverse interaction scenarios. To tackle this issue, we propose a Computational Interpersonal Communication Model (CICM) based on psychological theory to represent dyadic interpersonal communication as a stochastic process, providing a scenario-independent theoretical framework for evaluating autistic sociability. We apply CICM to the response-to-name (RTN) with 48 subjects, including 30 toddlers with ASD and 18 typically developing (TD), and design a joint state transition matrix as quantitative indicators. Paired with machine learning, our proposed CICM-driven indicators achieve consistencies of 98.44% and 83.33% with RTN expert ratings and ASD diagnosis, respectively. Beyond outstanding screening results, we also reveal the interpretability between CICM-driven indicators and expert ratings based on statistical analysis.

Hyper-reactivity to sensory inputs is a common and debilitating symptom of autism spectrum disorder (ASD), but the underlying neural abnormalities remain unclear. Two of three patients in our clinical cohort screen harboring de novo SHANK2 mutations also exhibited high sensitivity to visual, auditory, and tactile stimuli, so we examined whether shank2 deficiencies contribute to sensory abnormalities and other ASD-like phenotypes by generating a stable shank2b-deficient zebrafish model (shank2b^−/−). The adult shank2b^−/− zebrafish demonstrated reduced social preference and kin preference as well as enhanced behavioral stereotypy, while larvae exhibited hyper-sensitivity to auditory noise and abnormal hyperactivity during dark-to-light transitions. This model thus recapitulated the core developmental and behavioral phenotypes of many previous genetic ASD models. Expression levels of γ-aminobutyric acid (GABA) receptor subunit mRNAs and proteins were also reduced in shank2b^−/− zebrafish, and these animals exhibited greater sensitivity to drug-induced seizures. Our results suggest that GABAergic dysfunction is a major contributor to the sensory hyper-reactivity in ASD, and they underscore the need for interventions that target sensory-processing disruptions during early neural development to prevent disease progression.

Rett syndrome (RTT) is a neurodevelopmental disorder caused by loss-of-function variants in the MECP2 gene, currently with no cure. Neuroimaging is an important tool for obtaining non-invasive structural and functional information about the in vivo brain. Multiple approaches to magnetic resonance imaging (MRI) scans have been utilized effectively in RTT patients to understand the possible pathological basis. This study combined developmental evaluations with clinical severity, T1-weighted imaging, and diffusion tensor imaging, aiming to explore the structural alterations in cohorts of young girls with RTT, idiopathic autism spectrum disorder (ASD), or typical development. Voxel-based morphometry (VBM) was used to determine the voxel-wised volumetric characteristics of gray matter, while tract-based spatial statistics (SPSS) was used to obtain voxel-wised properties of white matter. Finally, a correlation analysis between the brain structural alterations and the clinical evaluations was performed. In the RTT group, VBM revealed decreased gray matter volume in the insula, frontal cortex, calcarine, and limbic/paralimbic regions; TBSS demonstrated decreased fractional anisotropy (FA) and increased mean diffusivity (MD) mainly in the corpus callosum and other projection and association fibers such as superior longitudinal fasciculus and corona radiata. The social impairment quotient and clinical severity were associated with these morphometric alterations. This monogenic study with an early stage of RTT may provide some valuable guidance for understanding the disease pathogenesis. At the same time, the pediatric-adjusted analytic pipelines for VBM and TBSS were introduced for significant improvement over classical approaches for MRI scans in children.

Objectives: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with genetic and clinical heterogeneity. Owing to the advancement of sequencing technologies, an increasing number of ASD-related genes have been reported. We designed a targeted sequencing panel (TSP) for ASD based on next-generation sequencing (NGS) to provide clinical strategies for genetic testing of ASD and its subgroups.Methods: TSP comprised 568 ASD-related genes and analyzed both single nucleotide variations (SNVs) and copy number variations (CNVs). The Autism Diagnostic Observation Schedule (ADOS) and the Griffiths Mental Development Scales (GMDS) were performed with the consent of ASD parents. Additional medical information of the selected cases was recorded.Results: A total of 160 ASD children were enrolled in the cohort (male to female ratio 3.6:1). The total detection yield was 51.3% for TSP (82/160), among which SNVs and CNVs accounted for 45.6% (73/160) and 8.1% (13/160), respectively, with 4 children having both SNVs and CNV variants (2.5%). The detection rate of disease-associated variants in females (71.4%) was significantly higher than that in males (45.6%, p = 0.007). Pathogenic and likely pathogenic variants were detected in 16.9% (27/160) of the cases. SHANK3, KMT2A, and DLGAP2 were the most frequent variants among these patients. Eleven children had de novo SNVs, 2 of whom had de novo ASXL3 variants with mild global developmental delay (DD) and minor dysmorphic facial features besides autistic symptoms. Seventy-one children completed both ADOS and GMDS, of whom 51 had DD/intellectual disability (ID). In this subgroup of ASD children with DD/ID, we found that children with genetic abnormalities had lower language competence than those without positive genetic findings (p = 0.028). There was no correlation between the severity of ASD and positive genetic findings.Conclusion: Our study revealed the potential of TSP, with lower cost and more efficient genetic diagnosis. We recommended that ASD children with DD or ID, especially those with lower language competence, undergo genetic testing. More precise clinical phenotypes may help in the decision-making of patients with genetic testing.